---
language:
- en
tags:
- text-classification
- zero-shot-classification
pipeline_tag: zero-shot-classification
library_name: transformers
---
# deberta-v3-base-zeroshot-v1

## Model description
The model is designed for zero-shot classification with the Hugging Face pipeline. 
The model should be substantially better at zero-shot classification than my other zero-shot models on the
Hugging Face hub: https://huggingface.co/MoritzLaurer.

The model can do one universal task: determine whether a hypothesis is `true` or `not_true` given a text.  
This task format is based on the Natural Language Inference task (NLI).
This task is so universal that any classification task can be reformulated into this true vs. false task.

The model was trained on a mixture of 27 tasks and 310 classes that have been reformatted into this universal format.
1.   26 classification tasks with ~400k texts:
['amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes',
'emotiondair', 'emocontext', 'empathetic',
'financialphrasebank', 'banking77', 'massive',
'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate', 
'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent',
'agnews', 'yahootopics',
'trueteacher', 'spam', 'wellformedquery',]
2.   Five NLI datasets with ~885k texts: ["mnli", "anli", "fever", "wanli", "ling"]

Note that compared to other NLI models, this model predicts two classes (true vs. not_true) as opposed to three classes (entailment/neutral/contradiction)


### How to use the model
#### Simple zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1")
sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)
```

### Details on data and training

The code for preparing the data and training & evaluating the model is fully open-source here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main

## Limitations and bias
The model can only do text classification tasks. 

Please consult the original DeBERTa paper and the papers for the different datasets for potential biases. 

## Citation
If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k.

### Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)

### Debugging and issues
Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.